WSIB presentation at the Chief Analytics Officer, Fall 2016

1. Workplace Safety and Insurance Board |Commissionde la sécurité Machine Learning – The Value add to Our Services Christina Hoy VP, Corporate Business Information and Analytics October 5, 2016
2. Workplace Safety andInsuranceBoard |Commissionde la sécurité WSIB – Who we are 2  A “hybrid” – independent trust agency in Ontario, Canada: - legislated by the Ontario government and responsible for administering the Workplace Safety and Insurance Act (WSIA) - funded by the employers of Ontario  Administers compensation and no-fault insurance for Ontario workplaces  Serves workers and employers ranging from small businesses to large private- and public- sector organizations Source: By the Numbers: 2015 WSIB Statistical Report www.wsibstatistics.ca
3. Workplace Safety andInsuranceBoard |Commissionde la sécurité Then and Now – Strategic Change at WSIB 3
4. Workplace Safety andInsuranceBoard |Commissionde la sécurité Defining Machine Learning
5. Workplace Safety andInsuranceBoard |Commissionde la sécurité Machine Learning at WSIB 5 Operationalizing Machine Learning can transformWSIB, moving beyond monitoring, to delivering new value added services Machine learning means strategically investing in advanced analytics tools that add value to WSIB. Machine Learning will enable WSIB to: – Bring effective innovative solutions to better serve our customers and improve outcomes, – Improve decision making in real-time by embedding machine learning into business decisions made at thefront-lines of our operations, – Build a culture of continuous improvement by implementinga feedback loopbetween decision makers and senior management, and – Scale up the use of data by pulling together informationfrom a number of disparate sources to create models that better target customer segments and geographies to producehighly specific products/services for our customers.
6. Workplace Safety andInsuranceBoard |Commissionde la sécurité Machine Learning
7. Workplace Safety andInsuranceBoard |Commissionde la sécurité Examples of Machine Learning at WSIB The following are early examples of machine learning: ■ Claims Risk Scoring ■ Fraud Detection – Employer Premium – Service Provider 7 Machine Learning at WSIB implements a continuous feed back loop
8. Workplace Safety andInsuranceBoard |Commissionde la sécurité Claims Risk Scoring Current: ■ Claims risk scoring answers the question; howdo changes to different factors influence the outcome of a claim at a specific time after injury? ■ In order to address this question our data scientists worked collaboratively with senior management to understand the business issue. ■ Once the issue was understood, a reviewof the available data tookplace. ■ We then used SAS tools to bring this data together from a number of disparate sources. ■ Finally, a model was created and supervised machine learning techniques were applied. ■ The business reviewed the outputs of this model for acceptance and we then successfully deployed to middle management for use in prioritizing the reviewof claims. Future: ■ Our plan is to deploy this model to front-line decision makers by integrating directly into our front-line systems. ■ By doing so we hope to implement a decision management system for feedback loop to assess assess the impact of decisions made.
9. Workplace Safety andInsuranceBoard |Commissionde la sécurité Claim Risk Scoring Example Joe is a 45 year old male. He is currently employed in the Forestry industry and is an equipment operator. Joe is a French speaking Canadian who has never had a prior claim. Joe works for a private company with less than 20 employees and makes approx. $500 per week. Last week Joe fell at work and was injured. Back Sprain Shoulder Leg fracture 8 Probability of staying on benefits changes for different injury types
10. Workplace Safety andInsuranceBoard |Commissionde la sécurité Fraud Detection 10 Current: ■ How do we better understand and identifyfraud, waste and abuse. ■ To do so we created two fraud detection models to use as a targeting tool for high risk employers and service provides:  Employer Premium Reporting, and  Service Provider. ■ Once we understood the business issue we reviewed both internal and external data and combined them using specialized software. ■ A model was then created and we applied both unsupervised and supervised machine learning techniques. ■ The outputs of this model were then reviewed by our front-line business for acceptance, at which point we deployed in the form of reports and target lists. Future: ■ As with our older model we plan to deploy this to front-line decision makers which will enable a more robust feedback loop on decisions.
11. Workplace Safety andInsuranceBoard |Commissionde la sécurité How did we embed this into our operations? 11 Formal policies, systems and practices Informal practices, and symbolic actions Belief, values and attitudes The key to success is to create an Analytics Driven Culture
12. Workplace Safety andInsuranceBoard |Commissionde la sécurité Building the right team 12 At least four different skill streams are required: Design the necessary infrastructure Utilizing data produced by data science Wrangling data from data marts and warehouses Constructs, designs or arranges usable data
13. Workplace Safety andInsuranceBoard |Commissionde la sécurité Continuous Improvement: the Machine Learning Journey ■ Continue culture shift among senior management by building trust through small scale focused initiatives with high potential successes. ■ Embed machine learning by implementing these models into the tools that front-line decision makers use. ■ Fill technological gaps by strategically investing in new tools and products that enable the use of machine learning techniques more effectively. ■ Continue to build in invest in our Information Management Architecture. 13 WSIB will strive to create an environment of analytics throughout the organization to spur creativity in how we solve seen and unseen problems
14. Workplace Safety andInsuranceBoard |Commissionde la sécurité Our Journey Continued… ■ Continual improvement of data quality and access by implementing a data Quality Assurance Program. ■ Change in team structure to ensure our workforce has the right capability to meet the needs of a data driven future. ■ Drive innovation in data analytics by aligning a strategic team of experts with a common focus on maximizing business objectives through a Centre of Excellence. 14 WSIB will strive to create an environment of analytics throughout the organization to spur creativity in how we solve seen and unseen problems
15. Workplace Safety andInsuranceBoard |Commissionde la sécurité Questions 13